SONG Minfeng, HE Xiufeng, WANG Xiaolei, LI Weiqiang. A New Initialization Method for Specular Points and Space Paths Computation in Spaceborne GNSS-R[J]. Geomatics and Information Science of Wuhan University, 2024, 49(1): 131-138. DOI: 10.13203/j.whugis20220789
Citation: SONG Minfeng, HE Xiufeng, WANG Xiaolei, LI Weiqiang. A New Initialization Method for Specular Points and Space Paths Computation in Spaceborne GNSS-R[J]. Geomatics and Information Science of Wuhan University, 2024, 49(1): 131-138. DOI: 10.13203/j.whugis20220789

A New Initialization Method for Specular Points and Space Paths Computation in Spaceborne GNSS-R

More Information
  • Received Date: December 21, 2022
  • Available Online: May 09, 2023
  • Objectives 

    In response to the problem of large initialization errors in specular point and geometric path computations in earth observation technology using global navigation satellite system-reflectometry(GNSS-R), an efficient new method for initializing multi-system spaceborne GNSS-R specular point estimation and geometric path computation is proposed.

    Methods 

    Based on a large number of simulated spaceborne GNSS-R reflection events, the ratio of the distance, between the projection point of the truth specular point on the direct path of the signal to the two satellites, and the geocentric angle of the reflection event under fixed orbit conditions is modeled using polynomials. Furthermore, the variation trend of these model coefficients with different GNSS satellite orbit heights is fitted to obtain a model of the variation of parameter coefficients with GNSS orbit heights, thus establishing the new initialization method.

    Results and Conclusions 

    The results of experiments based on multi-system multi-orbit GNSS-R simulation data show that the initialization accuracy of the new model is improved from hundreds or even thousands of kilometers to about 5 km compared with the existing methods, and the comparison experiments also demonstrate that the proposed method could improve the calculation efficiency of the mainstream iterative method by 79.1% in grazing observation.

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